Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images

Research output: A Conference proceeding or a Chapter in BookConference contribution

4 Citations (Scopus)

Abstract

In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.
Original languageEnglish
Title of host publicationInternational conference on Neural Information Processing (ICONIP 2012)
Subtitle of host publicationLecture Notes in Computer Science
EditorsTingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
Place of PublicationGermany
PublisherSpringer
Pages465-472
Number of pages8
Volume7667
ISBN (Electronic)9783642345005
ISBN (Print)9783642344992
DOIs
Publication statusPublished - 2012
Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

Conference19th International Conference on Neural Information Processing 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

Fingerprint

Magnetic resonance
Brain
Neural networks
Learning systems
Feature extraction
Classifiers
Neuroimaging
Research laboratories
Wavelet transforms
Testing

Cite this

Singh, L., Chetty, G., & Sharma, D. (2012). Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science (Vol. 7667, pp. 465-472). Germany: Springer. https://doi.org/10.1007/978-3-642-34500-5_55
Singh, Lavneet ; Chetty, Girija ; Sharma, Dharmendra. / Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. editor / Tingwen Huang ; Zhigang Zeng ; Chuandong Li ; Chi Sing Leung. Vol. 7667 Germany : Springer, 2012. pp. 465-472
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title = "Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images",
abstract = "In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.",
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}

Singh, L, Chetty, G & Sharma, D 2012, Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. in T Huang, Z Zeng, C Li & CS Leung (eds), International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. vol. 7667, Springer, Germany, pp. 465-472, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34500-5_55

Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. / Singh, Lavneet; Chetty, Girija; Sharma, Dharmendra.

International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. ed. / Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi Sing Leung. Vol. 7667 Germany : Springer, 2012. p. 465-472.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images

AU - Singh, Lavneet

AU - Chetty, Girija

AU - Sharma, Dharmendra

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N2 - In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.

AB - In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based on deep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.

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BT - International conference on Neural Information Processing (ICONIP 2012)

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Singh L, Chetty G, Sharma D. Using Hybrid Neural Networks for Identifying the Brain Abnormalities from MRI Structural Images. In Huang T, Zeng Z, Li C, Leung CS, editors, International conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. Vol. 7667. Germany: Springer. 2012. p. 465-472 https://doi.org/10.1007/978-3-642-34500-5_55